Earticle

초록

영어

Fuzzy neural network(FNN) is a neural network based on combining the advantages of the fuzzy theory and neural network. It has the characteristics of dealing with the non-linear and fuzziness and so on. Particle swarm optimization(PSO) algorithm is a population-based search algorithm by simulating the social behavior of birds within a flock. So the quantum PSO(QPSO) algorithm is proposed for optimizing the parameters of FNN in order to construct a new hybrid optimization(QPSO-FNN) algorithm in this paper. In the proposed QPSO-FNN algorithm, the quantum theory is used to improve the PSO algorithm, then the global optimization ability of QPSO algorithm is optimize the parameters of FNN model by putting these parameters in the particle encoding. The found optimal values are regarded as the parameters of FNN model to obtain the final QPSO-FNN method. Finally, the QPSO-FNN algorithm is used to solve the complex problem, the experimental results show that the QPSO-FNN algorithm takes on the shorter response time and higher solving accuracy.

목차

Abstract
 1. Introduction
 2. The PSO and Quantum PSO (QPSO)
  2.1. The PSO Agorithm
  2.2. The QPSO Agorithm
 3. The FNN Model
 4. A New Hybrid Optimization (QPSO-FNN) Algorithm
 5. Digital Simulation and Performance Analysis
 6. Conclusion
 Acknowledgments
 References

키워드

저자

• Meng Liu [ College of Electronic Information and Electrical Engineering, Shangluo University, Shangluo 726000,Shanxi,China ]
• Jiayun Zhang [ Mechanical and Electrical Engineering Institute, Rizhao Polytechnic, Rizhao 276826,Shandong,China ]
• Yazi Wang [ School of Mathematics and Statistics, ZhouKou Normal University,Henan,466001, China ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605